Temporal Orientation and Customer Loyalty Programs
Bibliographic record
Abstract
Loyalty programs play a prominent role in many firms’ customer relationship management programs, but not all programs are successful. Providers need to understand not only what benefits customers want in a program, but also how they want to be treated as a loyalty member. We posit that because loyalty programs offer rewards that are time-bound (immediate or delayed), and that loyalty programs seek to develop a relationship that extends over time, an important, but overlooked dimension for hospitality managers to consider is how their customers view time. Our research focuses on customers’ temporal orientation—the tendency to think in the present, future, or past. We use depth interviews to explore existing casino loyalty program participants’ thoughts and feelings about their ideal loyalty program. We find the customers’ temporal orientation influences the type of relationship as well as the type of benefits sought in the loyalty program. Our research offers managerially practical insights for identifying customers more likely to engage in co-production of a long-term loyalty relationship as well as for creating communication strategies that are likely to interest and provoke different temporal mindsets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".